Story Generation Using Knowledge Graph Under Psychological States

Story generation, aiming to generate a story that people could understand easily, captures increasing researchers' attention in recent years. However, a good story usually requires interesting and emotional plots. Previous works only consider a specific or binary emotion like positive or negative. In our work, we propose a Knowledge-Aware Generation framework under Controllable CondItions (K-GuCCI). The model assigns a change line of psychological states to story characters, which makes the story develop following the setting. Besides, we incorporate the knowledge graph into the model to facilitate the coherence of the story. Moreover, we investigate a metric AGPS to evaluate the accuracy of generated stories' psychological states. Experiments exhibit that the proposed model improves over standard benchmarks, while also generating stories reliable and valid.